DEVELOPMENT... OpenML
Run
159642

Run 159642

Task 2079 (Supervised Classification) eucalyptus Uploaded 12-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I80
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.8994
Per class
F measure
0.6473
Per class
Kappa
0.5486
KB relative information score
422.5875
Mean absolute error
0.1586
Mean prior absolute error
0.3132
Number of instances
736
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6474
Per class
Predictive accuracy
0.6481
Prior entropy
2.2629
Recall
0.6481
Per class
Relative absolute error
0.5065
Root mean prior squared error
0.3957
Root mean squared error
0.3157
Root relative squared error
0.7977
SciMark benchmark
933.1776
UserCPU time millis
850
UserCPU time millis training
850